Wan Vace
Wan VACE is an advanced open source AI video generator developed by Alibaba’s Tongyi Lab in 2025. It supports text-to-video, image-to-video, and video editing tasks within one unified model. Available in 1.3B and 14B parameter sizes, Wan VACE offers high-quality AI video generation with multi-modal control like masks, poses, and flow. It's fully open-source under the Apache-2.0 license and used widely for research and creative applications in video generative AI.
PROMPT 1
"A hyperrealistic video of an interview in a professional TV studio. A tall Indian basketball player, nearly 3 meters tall, sits on a modern chair, dressed in a tailored suit with subtle basketball-themed details. He is visibly towering, with expressive brown eyes and an athletic build. Next to him sits a professional female journalist with dark hair and stylish glasses, holding a microphone and a notebook. The journalist asks questions with an engaging, confident demeanor. Both appear under soft studio lighting, with a sleek background featuring a subtle basketball motif. The atmosphere is serious yet friendly, showing close-up shots of their faces, hands, and body language. The scene feels cinematic, realistic, and high-definition."
Show Observations
After using and testing
Wan Vace
for the given prompts. Below are the discovered pros and cons for
Wan Vace
.
Pros
All-in-one generation & editing: supports text-to-video, image-to-video, video-to-video, and masked editing in one unified model
Open-source and freely accessible with Apache‑2.0 license, hosted on GitHub, ModelScope, and Hugging Face
Multi-modal control via mask, pose, depth, flow, layouts — enhances prompt fidelity and creative direction
Performance-scaled sizes: 1.3 B model works on consumer GPUs (~8 GB VRAM), 14 B variant supports HD generation and advanced editing
Cons
Resource-intensive: full-scale 14 B version requires high compute (multi-GPU or premium hardware)
Complex setup: relies on command-line, Gradio, or ComfyUI — not plug-and-play for non-technical users
Documentation still growing: while core code and examples exist, full tutorial coverage is limited compared to commercial tools
Output consistency varies with prompt design and hardware; may need iterative refinement